The amount of web content is vast and continuously increasing. Generally, forms of web content can include images, text and video. Currently, search engine applications on the Internet respond to a user query with results from an index of web content. The user then manually combs through the search results to find relevant web content. With the amount of web searching increasing, and being performed using devices such as mobile cell phones with limited input/output capabilities, manual aspects of search engine applications can be cumbersome and time consuming. Further, the user may wish to collect searched result to summarize a concept corresponding to the user query.
Again, with growing data content, the problem of summarization has received significant attention in the web, multimedia and computer vision communities. The summarization differs along various dimensions such as the media collection used, application information such as image features, properties and techniques that may be utilized to summarize a concept.
One possible solution is to construct such a summary manually by editors, by looking at the images and their descriptions. However, such a process for constructing the summaries is time consuming and expensive. Further, it is difficult to get an unbiased summary with such manual construction of the summaries. Also, such a process is strenuous to discover all topics or semantic aspects expressed in a large collection of images. Moreover, the process is not scalable because the number of collections and the size of each collection are too large, or different summarizations might be needed for the same concept to enable customization to user preferences.
For visual summarization, a clustering based method may find a representative set of images for each topic separately. Due to this, an image may occur multiple times in a summary over the topics. This affects ‘diversity’ property in the summary and is suboptimal when the summary size is small.
Further the user may be required to specify various additional constraints to summarize the concept. For example, the user may specify additional properties such as preferred topic and temporal distributions in the summary. However, no system exists in the related art to summarize the concept by considering such additional constraints that may be defined by the user.
Based on the foregoing, there is a need for a method and a system for efficiently summarizing a concept. Moreover, the summarized concept should possess properties to cover all relevant information corresponding to the concept. Furthermore, the method and the system should facilitate the user with flexibility in specifying various distribution constraints in the summarized concept.